Hari Prasanna Das
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PhD Scholar Department of Electrical Engineering and Computer Sciences University of California, Berkeley Email: hpdas (at) berkeley.edu Office: CREST Lab, 406 Cory Hall ResearchGate |
Brief Biography
I am a 3rd year graduate student in the Department of Electrical Engineering and Computer Sciences at University of California, Berkeley, advised by Prof. Costas J. Spanos. I am also a Graduate Student Researcher for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) program. My research interests are probabilistic machine learning, including topics in generative modeling, approximate inference and deep learning. I am also excited in grounding my research via applications relating to smart buildings and climate change.
I graduated from Indian Institute of Technology (IIT) Kharagpur with a B.Tech.(Honors) in Electrical Engineering in 2016. During my undergraduate studies, I was fortunate to work with Prof. Ashok Pradhan on several projects. Prior to joining UC Berkeley, I worked for a year at Mentor Graphics, India at Noida as a R&D Engineer.
News
February 2020: I am co-organizing the Computational Sustainability Open Graduate Seminar (COGS) series for Spring 2020. The seminars will be every alternate Friday 1.30PM-2.30PM Eastern Time, starting 07th Feb 2020. Please join the meeting if you are interested. [Link to join the seminar]
January 2020: We are organizing "2nd International Workshop on Applied Machine Learning for Intelligent Energy Systems (AMLIES)", co-located with ACM e-Energy Conference 2020 at Melbourne, Australia. Please consider submitting your work. [Call for Papers]
October 2019: Our paper "Design, Benchmarking and Graphical Lasso based Explainability Analysis of an Energy Game-Theoretic Framework" has been accepted at NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning.
October 2019: Our paper "A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks" has been accepted at ICMLA 2019 Special Session on Machine Learning in Energy Application.
September 2019: Selected to attend Doctoral Consortium on Computational Sustainability at CMU, with a travel funding from NSF.
August 2019: I am in the Technical Program Committee (TPC) of NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning.
August 2019: I am a Graduate Student Instructor (GSI) for the upper division class "EECS 127/227AT: Optimization Models in Engineering" in its Fall 2019 offering with Prof. Alexandre M. Bayen.
July 2019: Our paper "Personal thermal comfort models with wearable sensors" has been accepted for publication in the Building and Environment Journal.
May 2019: Our paper "Machine Learning empowered Occupancy Sensing for Smart Buildings" has been accepted at Climate Change and AI Workshop, International Conference on Machine Learning (ICML) 2019
April 2019: Our paper "WiFi and Vision Multimodal Learning for Accurate and Robust Device-Free Human Activity Recognition" has been accepted at MULA Workshop, Conference on Computer Vision and Pattern Recognition (CVPR) 2019
January 2019: We are organizing "International Workshop on Applied Machine Learning for Intelligent Energy Systems (AMLIES)", co-located with ACM e-Energy Conference 2019 at Phoenix, Arizona, USA. Please consider submitting your work. [Call for Papers]
December 2018: I am invited to attend the Global Young Scientists Summit (GYSS), 2019 to be held at Singapore in January 2019
November 2018: Our paper "Consensus Adversarial Domain Adaptation" has been accepted at AAAI Conference on Artificial Intelligence 2019
June 2018: Our paper "BISCUIT: Building Intelligent System CUstomer Investment Tool" has been accepted at International Conference on Applied Energy 2018
January 2018: Our paper "Personal thermal comfort models based on physiological parameters measured by wearable sensors" has been accepted at Windsor Conference 2018
